Paper year
2025
Paper dossier
Audio Signal Processing in the Artificial Intelligence Era: Challenges and Directions
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
3
Authors
7
Topic labels
2
Source readout
Source and corpus status
Venue
Journal of the Audio Engineering Society
Source slug
jaes
Corpus placement
Core corpus
Similarity rows
Not available yet
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
3
Top 50
1
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
In top 50 at rank 20
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8457, citation_velocity=0.1800, topic_growth=0.6943, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1691)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0900)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.2083)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.1800, topic_growth=0.6943, diversity_penalty=0.3333
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0630)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.4513)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.0667)
Under-cited
Present in run, outside top 50
Low-cite candidate pool (see docs/candidate-pool-low-cite.md v0): core corpus, recency floor, citation ceiling, title+abstract gate; popularity penalty among pool members only. Semantic and bridge not yet modeled.
Signals: citation_velocity=0.1800, topic_growth=0.6943, diversity_penalty=0.5579
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0540)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.4860)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Pool popularity penaltypenalty
Pool popularity penalty: reduces score when non-zero (contribution to score: -0.1395)
Abstract
Artificial intelligence (AI) has seen significant advancement in recent years, leading to increasing interest in integrating these techniques to solve both existing and emerging problems in audio engineering. In this paper, the authors investigate current trends in the application of AI for audio engineering, outlining open problems and applications in the research field. The paper begins by providing an overview of AI-based algorithm development in the context of audio, discussing problem selection and taxonomy. Next, human-centric AI challenges and how they relate to audio engineering are explored, including ethics, trustworthiness, explainability, and interaction, emphasizing the need for ethically sound and human-centered AI systems. Subsequently, technical challenges that arise when applying modern AI techniques to audio are examined, including robust generalization, audio quality, high sample rates, and real-time processing with low latency. Finally, the authors outline applications of AI in audio engineering, covering the development of machine learning-powered audio effects, synthesizers, automated mixing systems, and spatial audio, speech enhancement, dialog separation, and music generation. Emphasized are the need for a balanced approach that integrates humancentric concerns with technological advancements, advocating for responsible and effective application of AI.
Authors
Neighborhood labels
Topics
2 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music and Audio ProcessingSpeech and Audio Processing
Neighbor surface
Similar papers
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
No embedding-backed neighbors available for this paper/version yet.
Next handoff
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02
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03
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